title: Protein complex prediction in large ontology attributed protein-protein interaction networks.
abstract: Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.
authors.affiliation: Dalian University of Technology, Dalian.
authors.forename: Yijia
authors.initials: Y
authors.lastname: Zhang
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lin
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Yang
authors.forename: Jian
authors.initials: J
authors.lastname: Wang
authors.forename: Yanpeng
authors.initials: Y
authors.lastname: Li
authors.forename: Bo
authors.initials: B
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005656:Fungal Proteins / Q000737:chemistry / Q000378:metabolism; D060066:Protein Interaction Maps / Q000502:physiology*; D015003:Yeasts
chemical_list: D005656:Fungal Proteins